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When the level of congruence among all distance matrices is low, researchers can decide to analyse the matrices separately or in subgroups.
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For example, prior knowledge regarding the linear correlation of the data may suggest that the requested matrix is low rank.
The high sensitivity is achieved by conducting absorption measurements in the NIR region where background interference from the biological matrix is low.
However, by adequately sampling the parameter space, a good performance can be achieved as long as the mutual coherence among the columns of the measurement matrix is low enough to permit the sparse signal reconstruction.
The periphery of this matrix is low in PG but Gruber's stain shows it is positive for collagen staining.
Although comparing variance components and thus heritabilities is fraught with issues because of high standard errors, examination of Table 3 shows that the proportion of variation accounted for by the additive genomic relationship matrix is low relative to the entry-mean broad-sense heritability in most cases.
Furthermore, these approaches cannot reduce the dependence on the number of individuals studied to a linear time; rather they can at best reduce it to a quadratic one (Chen et al., 2013; Oualkacha et al., 2013) even when the similarity matrix is low rank.
The standard matrix completion techniques for recovering the user-item preference matrix assume that the true underlying matrix is low-rank.
The most common assumption is that the matrix is low-rank, i.e. M = WH T, where W ∈ ℝ N g × k and H ∈ ℝ N d × k are of rank k ≪ m, n.
Specifically, we also assume that the implicit feedback matrix is not globally low rank but some sub-matrices are low rank.
In this paper, we assume that the implicit feedback matrix has local property, where the original matrix is not globally low rank but some sub-matrices are low rank.
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CEO of Professional Science Editing for Scientists @ prosciediting.com